Fault detection and diagnosis of technical systems, 6 credits

Feldetektion och diagnos av tekniska system, 6 hp

TSFS22

Main field of study

Electrical Engineering

Course level

Second cycle

Course type

Programme course

Examiner

Daniel Jung

Director of studies or equivalent

Johan Löfberg

Education components

Preliminary scheduled hours: 52 h
Recommended self-study hours: 108 h

Available for exchange students

Yes
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, French 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, French (Control and Information Systems) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, French (Mechanics and Control) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, German 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, German (Control and Information Systems) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, German (Mechanics and Control) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Japanese 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Japanese (Control and Information Systems) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Japanese (Mechanics and Control) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Spanish 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Spanish (Control and Information Systems) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYI Applied Physics and Electrical Engineering - International, Master of Science in Engineering, Spanish (Mechanics and Control) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYY Applied Physics and Electrical Engineering, Master of Science in Engineering 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYY Applied Physics and Electrical Engineering, Master of Science in Engineering (Control and Information Systems) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CYYY Applied Physics and Electrical Engineering, Master of Science in Engineering (Mechanics and Control) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CDDD Computer Science and Engineering, Master of Science in Engineering 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CDDD Computer Science and Engineering, Master of Science in Engineering (AI and Machine Learning) 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CDDD Computer Science and Engineering, Master of Science in Engineering (Autonomus systems) 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CMJU Computer Science and Software Engineering, Master of Science in Engineering 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CMJU Computer Science and Software Engineering, Master of Science in Engineering (AI and Machine Learning) 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CITE Information Technology, Master of Science in Engineering 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CITE Information Technology, Master of Science in Engineering (AI and Machine Learning) 9 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CMMM Mechanical Engineering, Master of Science in Engineering 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E
6CMMM Mechanical Engineering, Master of Science in Engineering (Mechatronics) 7 (Autumn 2025) 2 2 Swedish/English Linköping, Valla E

Main field of study

Electrical Engineering

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Master of Science in Applied Physics and Electrical Engineering
  • Master of Science in Mechanical Engineering
  • Master of Science in Applied Physics and Electrical Engineering - International
  • Master of Science in Computer Science and Engineering
  • Master of Science in Information Technology
  • Master of Science in Computer Science and Software Engineering

Prerequisites

Automatic Control, Probability theory

Intended learning outcomes

To give both a theoretical and practical basis for how to design systems that automatically detect and isolate faulty components in technical processes.
 

After completing the course, the student shall  be able to:

  1. Based on a mathematical model of a technical process, apply model-based methods to analyze diagnostic performance and to detect and isolate faults.
  2. Based on historical data from a technical process apply data-driven methods to detect and classify faults.

Course content

1. Introduction to fault diagnosis, design of diagnostic systems, examples of industrial applications.
2. Mathematical modeling for fault detection and fault isolation using models, consistency relations, analytical redundancy.
3. Structural methods for fault diagnosis, bipartite graphs, modeling for structural analysis, matching, analysis of structural diagnosis properties, algorithms for finding overdetermined equation sets for residual generation.
4. Linear and nonlinear residual generation, observers and Kalman filters for diagnosis.
5. Statistical methods for fault detection.
6. Fault isolation, decisions with structured hypothesis tests, minimal hitting set.
7. Data-driven fault diagnosis, anomaly detection, classification.
8. Hybrid fault diagnosis combining model-based and data-driven diagnosis methods.

Teaching and working methods

The course is organized in lectures, problem solving sessions, and laborations.

Examination

LAB1Laboratory work2 creditsU, G
DAT1Computer exam4 creditsU, 3, 4, 5

Grades

Four-grade scale, LiU, U, 3, 4, 5

Department

Institutionen för systemteknik

Course literature

Compendia

  • Mattias Nyberg och Erik Frisk, Model Based Diagnosis of Technical Processes
Code Name Scope Grading scale
LAB1 Laboratory work 2 credits U, G
DAT1 Computer exam 4 credits U, 3, 4, 5

Compendia

Mattias Nyberg och Erik Frisk, Model Based Diagnosis of Technical Processes

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)
X
DAT1
calculus, algebra, probability theory
1.2 Fundamental engineering knowledge (G1X level)
X
DAT1
signal processing, logics
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)
X
LAB1
DAT1
automatic control, modeling
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)
X
LAB1
DAT1
fault diagnosis, machine learning
1.5 Insight into current research and development work
X
Information on current research activities
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving
X
LAB1
DAT1
Engineering trade-offs during design of diagnosis systems
2.2 Experimentation, investigation, and knowledge discovery
X
LAB1
DAT1
Laborations using real data and realistic models. Comparing different methods for fault diagnosis 
2.3 System thinking
X
comprehensive view on design of diagnosis systems
2.4 Attitudes, thought, and learning
X
LAB1
DAT1
Work during exercise sessions and laborations
2.5 Ethics, equity, and other responsibilities

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork
X
group work
3.2 Communications
X
LAB1
written lab reports
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context
X
Examples of industrial applications
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management
X
LAB1
DAT1
Modeling, analysis of diagnosis properties
4.4 Designing
X
LAB1
DAT1
Design of diagnosis systems
4.5 Implementing
X
LAB1
Implementation of algorithms
4.6 Operating
X
Laborations using data from realistic problem scenarios
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects

                            

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